Diversity of plants in cocoa agroforests in the humid forest zone of Southern Cameroon

In the humid forest zone of Southern Cameroon, farmers generally associate cocoa with native and exotic trees in complex agroforestry systems. Despite the socio-economic and ecological importance of these systems, few studies have investigated their plant composition. We investigated tree composition of cocoa agroforests along a gradient of market access, population density and resource use intensity in the humid forest zone of southern Cameroon, comprising (i) the sub-region of Yaoundé, (ii) the sub-region of Mbalmayo, and (iii) the sub-region of Ebolowa. Market access, population density and resource use intensity all decreased from the first to the third sub-region. We quantified the diversity of tree species associated with cocoa within individual agroforests, among agroforests in the same region, and among the three sub-regions, and classified the tree species according to their main uses. A total of 9.1 ha belonging to 60 cocoa agroforests were inventoried in 12 villages. We encountered a total of 206 tree species with an average of 21 tree species per agroforest. In the more urbanized area around Yaoundé, agroforests were less diverse than in the other sub-regions. In all of the agroforests, food producing tree species tended to be more frequent than other species. Two thirds of the food trees were native forest species and one third was introduced. From Ebolowa to Yaoundé, the density of food producing trees doubled and the density of exotic food-producing species increased relative to native species. Some local species producing high-value non-timber forest products were found in the agroforests, but their density was far lower than that of exotic tree species. The agroforests also provide medicine, charcoal and other products for household consumption and sale. We conclude that unless there are specific efforts to promote local forest tree species in cocoa agroforests, these will progressively lose importance with increasing market access, population pressure and land use intensity.     

Determining environmental covariates which explain genotype environment interaction in winter wheat through probe genotypes and biadditive factorial regression

Genotype-environment interaction has been analyzed in a winter-wheat breeding network using bi-additive factorial regression models. This family of models generalizes both factorial regression and biadditive (or AMMI) models; it fits especially well when abundant external information is available on genotypes and/or environments. Our approach, focused on environmental characterization, was performed with two kinds of covariates: (1) deviations of yield components measured on four probe genotypes and (2) usual indicators of yield-limiting factors. The first step was based on the analysis of a crop diagnosis on four probe genotypes. Difference of kernel number to a threshold number (DKN) and reduction of thousand-kernel weight from a potential value (RTKW) were used to characterize the grain-number formation and the grain-filling periods, respectively. Grain yield was analyzed according to a biadditive factorial regression model using eight environmental covariates (DKN and RTKW measured on each of four probe genotypes). In the second step, the usual indicators of yield-limiting factors were too numerous for the analysis of grain yield. Thus a selection of a subset of environmental covariates was performed on the analysis of DKN and RTKW for the four probe genotypes. Biadditive factorial regression models involved environmental covariates related to each deviation and included environmental main effect, sum of water deficits, an indicator of nitrogen stress, sum of daily radiation, high temperature, pressure of powdery mildew and lodging. The correlations of each environmental covariate to the synthetic variates helped to discard those poorly involved in interaction (with | correlation | <0.3). The grain yield of 12 genotypes was interpreted with the retained covariates using biadditive factorial regression. The models explained about 75% of the interaction sums of squares. In addition, the biadditive factorial regression biplot gave relevant information about the interaction of the genotypes (interaction pattern and sensitivities to environmental covariates) with respect to the environmental covariates and proved to be interesting for such an approach. Received: 8 March 1999 / Accepted: 29 July 1999     

Ht31, a protein kinase A anchoring inhibitor, induces robust cholesterol efflux and reverses macrophage foam cell formation through ATP-binding cassette transporter A1

Macrophage foam cell is the predominant cell type in atherosclerotic lesions. Removal of excess cholesterol from macrophages thus offers effective protection against atherosclerosis. Here we report that a protein kinase A (PKA)-anchoring inhibitor, st-Ht31, induces robust cholesterol/phospholipid efflux, and ATP-binding cassette transporter A1 (ABCA1) greatly facilitates this process. Remarkably, we found that st-Ht31 completely reverses foam cell formation, and this process is ABCA1-dependent. The reversal is also accompanied by the restoration of well modulated inflammatory response to LPS. There is no detectable toxicity associated with st-Ht31, even when cells export up to 20% cellular cholesterol per hour. Using FRET-based PKA biosensors in live cells, we provide evidence that st-Ht31 drives cholesterol efflux by elevating PKA activity specifically in the cytoplasm. Furthermore, ABCA1 facilitates st-Ht31 uptake. This allows st-Ht31 to effectively remove cholesterol from ABCA1-expressing cells. We speculate that de-anchoring of PKA offers a novel therapeutic strategy to remove excess cholesterol from lipid-laden lesion macrophages.     

Metabolomics standards initiative: ontology working group work in progress

In this article we present the activities of the Ontology Working Group (OWG) under the Metabolomics Standards Initiative (MSI) umbrella. Our endeavour aims to synergise the work of several communities, where independent activities are underway to develop terminologies and databases for metabolomics investigations. We have joined forces to rise to the challenges associated with interpreting and integrating experimental process and data across disparate sources (software and databases, private and public). Our focus is to support the activities of the other MSI working groups by developing a common semantic framework to enable metabolomics-user communities to consistently annotate the experimental process and to enable meaningful exchange of datasets. Our work is accessible via a public webpage and a draft ontology has been posted under the Open Biological Ontology umbrella. At the very outset, we have agreed to minimize duplications across omics domains through extensive liaisons with other communities under the OBO Foundry. This is work in progress and we welcome new participants willing to volunteer their time and expertise to this open effort. See the MSI Ontology Working Group website for a complete list of members and contributors. Web URL:     

Spatial segregation of specialists and generalists in bird communities

Each species generally has a close relationship with one or more habitats and can therefore be classified as either specialist or generalist. We studied whether specialist and generalist species are spatially distributed independently of each other. Repeating the analysis for 100 of the most frequent terrestrial bird species recorded over the 10 000 sampled sites of the French Breeding Bird survey, we found that specialists were more abundant if the rest of the community was specialized, and that the inverse was also true. This pattern was far subtler than just a simple dichotomy: most species actually presented a maximum abundance at a value of community specialization similar to their own level of specialization. Bird communities appear very well defined along a specialist–generalist gradient. We believe this pattern becomes more apparent with habitat degradation. The consequences on both ecological services and community resilience may well be considerable.     

Exenatide does not evoke pancreatitis and attenuates chemically induced pancreatitis in normal and diabetic rodents

The risk of developing pancreatitis is elevated in type 2 diabetes and obesity. Cases of pancreatitis have been reported in type 2 diabetes patients treated with GLP-1 (GLP-1R) receptor agonists. To examine whether the GLP-1R agonist exenatide potentially induces or modulates pancreatitis, the effect of exenatide was evaluated in normal or diabetic rodents. Normal and diabetic rats received a single exenatide dose (0.072, 0.24, and 0.72 nmol/kg) or vehicle. Diabetic ob/ob or HF-STZ mice were infused with exenatide (1.2 and 7.2 nmol·kg(-1)·day(-1)) or vehicle for 4 wk. Post-exenatide treatment, pancreatitis was induced with caerulein (CRN) or sodium taurocholate (ST), and changes in plasma amylase and lipase were measured. In ob/ob mice, plasma cytokines (IL-1β, IL-2, IL-6, MCP-1, IFNγ, and TNFα) and pancreatitis-associated genes were assessed. Pancreata were weighed and examined histologically. Exenatide treatment alone did not modify plasma amylase or lipase in any models tested. Exenatide attenuated CRN-induced release of amylase and lipase in normal rats and ob/ob mice but did not modify the response to ST infusion. Plasma cytokines and pancreatic weight were unaffected by exenatide. Exenatide upregulated Reg3b but not Il6, Ccl2, Nfkb1, or Vamp8 expression. Histological analysis revealed that the highest doses of exenatide decreased CRN- or ST-induced acute inflammation, vacuolation, and acinar single cell necrosis in mice and rats, respectively. Ductal cell proliferation rates were low and similar across all groups of ob/ob mice. In conclusion, exenatide did not modify plasma amylase and lipase concentrations in rodents without pancreatitis and improved chemically induced pancreatitis in normal and diabetic rodents.